This paper aims to review Artificial neural networks, Multi-Layer Perceptron Neural network
(MLP) and Convolutional Neural network (CNN) employed to detect breast malignancies for …

Breast cancer (BC) is one of the most common cancers among women worldwide,
representing the majority of new cancer cases and cancer-related deaths according to …

Breast cancer continues to be a significant public health problem in the world. Early
detection is the key for improving breast cancer prognosis. Mammography has been one of …

The American Cancer Society (ACS) recommends women aged 40 and above to have a
mammogram every year and calls it a gold standard for breast cancer detection. Early …

Machine learning offers a principled approach for developing sophisticated, automatic, and
objective algorithms for analysis of high-dimensional and multimodal biomedical data. This …

This paper presents an evolutionary artificial neural network (EANN) approach based on the
pareto-differential evolution (PDE) algorithm augmented with local search for the prediction …

The roles of physicists in medical imaging have expanded over the years, from the study of
imaging systems (sources and detectors) and dose to the assessment of image quality and …

Feature selection in breast cancer disease important and risky task for further analysis.
Breast cancer is the second leading reason for death among the women. Cancer starts from …

In this research, a hybrid model is developed by integrating a case-based data clustering
method and a fuzzy decision tree for medical data classification. Two datasets from UCI …

Machine-learning methods can assist with the medical decision-making processes at the
both the clinical and diagnostic levels. In this article, we first review historical milestones and …